| name | splitty |
| description | Run a chunk-level pipes-and-filters analysis end-to-end. Use when the user says "splitty <do something>", "split this corpus", "map-reduce this", "fan out X across Y", "run a pipeline over <files>", or describes a goal that requires processing many chunks of text in parallel and unioning the results. This skill designs a pipeline (if none is given), then drives it to completion by spawning sub-agents one step at a time. |
splitty (full lifecycle)
The user has given you a goal in natural language. You will:
- Decide whether to design a new pipeline or reuse an existing one.
- Run it to completion, dispatching sub-agents step by step.
This skill composes splitty-design (pipeline authoring) and splitty-run
(orchestration). If the user supplied a .yaml pipeline path or asked you
to use one of the examples in pipelines/examples/, skip design and go
straight to run.
Step 1 — locate or design the pipeline
Decision tree:
State your intent in one sentence before proceeding (e.g., "Designing a
classify-then-extract pipeline for your notes/ folder.").
Step 2 — run the pipeline
Invoke the splitty-run skill body (read its SKILL.md and follow the
procedure exactly). Do not improvise the orchestration loop — it is
deterministic by design.
Step 3 — show the result
After splitty finalize, read the result file from the path it printed and
present a brief summary to the user (1-3 sentences) plus the path. Do not
re-summarize the entire result; the user can read it.
Notes on scope
- This skill orchestrates one pipeline run per invocation. If the user
wants multiple unrelated runs, run them serially or ask which to start.
- The orchestrator is Claude itself in the main loop. There is no daemon,
no MCP server, no background process. Each
Task() call you make spawns
one filter sub-agent for one step.
- Sub-agents never call
Task themselves. Fan-out is your job.
